import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class     lowerCAmelCase_     (       _lowerCAmelCase ):
							UpperCAmelCase				   =     42
							UpperCAmelCase				   =     None




def 					_snake_case	(  __snake_case  ,     __snake_case=0.999  ,     __snake_case="cosine"  ,     ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__snake_case						):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(__snake_case						):
															return math.exp(t * -12.0						)

					else:
										raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""						)

					_UpperCamelCase			     =     []
					for i in range(UpperCAmelCase_						):
										_UpperCamelCase			     =     i / num_diffusion_timesteps
										_UpperCamelCase			     =     (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_						) / alpha_bar_fn(UpperCAmelCase_						)  ,     UpperCAmelCase_						)						)
					return torch.tensor(UpperCAmelCase_  ,     dtype=torch.floataa						)







class     lowerCAmelCase_     (       _lowerCAmelCase,  _lowerCAmelCase ):







							@register_to_config
							def __init__(   self      :     Optional[int] ,			_A      :     int = 1000 ,			_A      :     str = "fixed_small_log" ,			_A      :     bool = True ,			_A      :     Optional[float] = 1.0 ,			_A      :     str = "epsilon" ,			_A      :     str = "squaredcos_cap_v2" ,			):
												if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''		)

												_UpperCamelCase			     =     betas_for_alpha_bar(UpperCAmelCase_		)

												_UpperCamelCase			     =     1.0 - self.betas
												_UpperCamelCase			     =     torch.cumprod(self.alphas ,			dim=0		)
												_UpperCamelCase			     =     torch.tensor(1.0		)

												# standard deviation of the initial noise distribution
												_UpperCamelCase			     =     1.0

												# setable values
												_UpperCamelCase			     =     None
												_UpperCamelCase			     =     torch.from_numpy(np.arange(0 ,			UpperCAmelCase_		)[::-1].copy()		)

												_UpperCamelCase			     =     variance_type







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     torch.FloatTensor ,			_A      :     Optional[int] = None		):
												return sample







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     int ,			_A      :     Union[str, torch.device] = None		):
												_UpperCamelCase			     =     num_inference_steps
												_UpperCamelCase			     =     (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
												_UpperCamelCase			     =     (np.arange(0 ,			UpperCAmelCase_		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
												_UpperCamelCase			     =     torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Tuple ,			_A      :     Optional[int]=None ,			_A      :     Any=None ,			_A      :     List[str]=None		):
												if prev_timestep is None:
																	_UpperCamelCase			     =     t - 1

												_UpperCamelCase			     =     self.alphas_cumprod[t]
												_UpperCamelCase			     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												_UpperCamelCase			     =     1 - alpha_prod_t
												_UpperCamelCase			     =     1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																	_UpperCamelCase			     =     self.betas[t]
												else:
																	_UpperCamelCase			     =     1 - alpha_prod_t / alpha_prod_t_prev

												# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
												# and sample from it to get previous sample
												# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
												_UpperCamelCase			     =     beta_prod_t_prev / beta_prod_t * beta

												if variance_type is None:
																	_UpperCamelCase			     =     self.config.variance_type

												# hacks - were probably added for training stability
												if variance_type == "fixed_small_log":
																	_UpperCamelCase			     =     torch.log(torch.clamp(UpperCAmelCase_ ,			min=1e-20		)		)
																	_UpperCamelCase			     =     torch.exp(0.5 * variance		)
												elif variance_type == "learned_range":
																	# NOTE difference with DDPM scheduler
																	_UpperCamelCase			     =     variance.log()
																	_UpperCamelCase			     =     beta.log()

																	_UpperCamelCase			     =     (predicted_variance + 1) / 2
																	_UpperCamelCase			     =     frac * max_log + (1 - frac) * min_log

												return variance







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     torch.FloatTensor ,			_A      :     int ,			_A      :     torch.FloatTensor ,			_A      :     Optional[int] = None ,			_A      :     int=None ,			_A      :     bool = True ,			):
												_UpperCamelCase			     =     timestep

												if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																	_UpperCamelCase     ,    _UpperCamelCase			     =     torch.split(UpperCAmelCase_ ,			sample.shape[1] ,			dim=1		)
												else:
																	_UpperCamelCase			     =     None

												# 1. compute alphas, betas
												if prev_timestep is None:
																	_UpperCamelCase			     =     t - 1

												_UpperCamelCase			     =     self.alphas_cumprod[t]
												_UpperCamelCase			     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												_UpperCamelCase			     =     1 - alpha_prod_t
												_UpperCamelCase			     =     1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																	_UpperCamelCase			     =     self.betas[t]
																	_UpperCamelCase			     =     self.alphas[t]
												else:
																	_UpperCamelCase			     =     1 - alpha_prod_t / alpha_prod_t_prev
																	_UpperCamelCase			     =     1 - beta

												# 2. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
												if self.config.prediction_type == "epsilon":
																	_UpperCamelCase			     =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												elif self.config.prediction_type == "sample":
																	_UpperCamelCase			     =     model_output
												else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																	    ''' for the UnCLIPScheduler.'''		)

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																	_UpperCamelCase			     =     torch.clamp(
																	    UpperCAmelCase_ ,			-self.config.clip_sample_range ,			self.config.clip_sample_range		)

												# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
												# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
												_UpperCamelCase			     =     (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
												_UpperCamelCase			     =     alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

												# 5. Compute predicted previous sample µ_t
												# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
												_UpperCamelCase			     =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

												# 6. Add noise
												_UpperCamelCase			     =     0
												if t > 0:
																	_UpperCamelCase			     =     randn_tensor(
																	    model_output.shape ,			dtype=model_output.dtype ,			generator=UpperCAmelCase_ ,			device=model_output.device		)

																	_UpperCamelCase			     =     self._get_variance(
																	    UpperCAmelCase_ ,			predicted_variance=UpperCAmelCase_ ,			prev_timestep=UpperCAmelCase_ ,			)

																	if self.variance_type == "fixed_small_log":
																						_UpperCamelCase			     =     variance
																	elif self.variance_type == "learned_range":
																						_UpperCamelCase			     =     (0.5 * variance).exp()
																	else:
																						raise ValueError(
																						    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																						    ''' for the UnCLIPScheduler.'''		)

																	_UpperCamelCase			     =     variance * variance_noise

												_UpperCamelCase			     =     pred_prev_sample + variance

												if not return_dict:
																	return (pred_prev_sample,)

												return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase_ ,			pred_original_sample=UpperCAmelCase_		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     torch.FloatTensor ,			_A      :     torch.FloatTensor ,			_A      :     torch.IntTensor ,			):
												_UpperCamelCase			     =     self.alphas_cumprod.to(device=original_samples.device ,			dtype=original_samples.dtype		)
												_UpperCamelCase			     =     timesteps.to(original_samples.device		)

												_UpperCamelCase			     =     alphas_cumprod[timesteps] ** 0.5
												_UpperCamelCase			     =     sqrt_alpha_prod.flatten()
												while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
																	_UpperCamelCase			     =     sqrt_alpha_prod.unsqueeze(-1		)

												_UpperCamelCase			     =     (1 - alphas_cumprod[timesteps]) ** 0.5
												_UpperCamelCase			     =     sqrt_one_minus_alpha_prod.flatten()
												while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
																	_UpperCamelCase			     =     sqrt_one_minus_alpha_prod.unsqueeze(-1		)

												_UpperCamelCase			     =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
												return noisy_samples


"""simple docstring"""


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Any				=      VQModel
       UpperCamelCase_		:       Dict				=      '''sample'''






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				List[str]=(32, 32)      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		4
              UpperCamelCase       			=		3

              UpperCamelCase       			=		floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCAmelCase_      )

              return {"sample": image}






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              return (3, 32, 32)






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              return (3, 32, 32)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		{
                  "block_out_channels": [32, 64],
                  "in_channels": 3,
                  "out_channels": 3,
                  "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                  "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "latent_channels": 3,
              }
              UpperCamelCase       			=		self.dummy_input
              return init_dict, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"							,   output_loading_info=UpperCAmelCase_      )
              self.assertIsNotNone(UpperCAmelCase_      )
              self.assertEqual(len(loading_info["missing_keys"]      )							,   0      )

              model.to(UpperCAmelCase_      )
              UpperCamelCase       			=		model(**self.dummy_input      )

              assert image is not None, "Make sure output is not None"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"      )
              model.to(UpperCAmelCase_      ).eval()

              torch.manual_seed(0      )
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0      )

              UpperCamelCase       			=		torch.randn(1							,   model.config.in_channels							,   model.config.sample_size							,   model.config.sample_size      )
              UpperCamelCase       			=		image.to(UpperCAmelCase_      )
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_      ).sample

              UpperCamelCase       			=		output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              UpperCamelCase       			=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
              # fmt: on
              self.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )





